What is Data Lake?
A data lake is a centralized repository that stores structured, semi-structured, and unstructured data at any scale, enabling organizations to collect data in its raw form without the need for immediate structuring or processing. Unlike traditional data warehouses, which require predefined schemas, data lakes provide flexibility by allowing data to remain in its native format until needed. This flexibility makes data lakes particularly valuable in modern analytics, where diverse data sources such as IoT devices, social media, and logs need to be analyzed together.
What is Redshift?
Amazon Redshift is the cloud data lake offering by AWS. It is using Postgress engine at the backend.
Redshift offers lightning-fast query execution through massively parallel processing (MPP) and columnar storage, making it ideal for handling complex analytical workloads across large datasets. It also has got seamless integration with a lot of other AWS ecosystem tools.
Cost issues with Redshift
One big concern with Redshift is it’s cost. The pricing model includes charges for compute nodes, storage, and additional features like Redshift Spectrum, which can quickly add up for large-scale or unpredictable data needs. Moreover, the upfront commitment required for reserved instances to achieve lower pricing can be restrictive for businesses seeking flexibility. For companies with smaller budgets or inconsistent query volumes, the cost-per-query and storage charges may outweigh the benefits, prompting them to explore more cost-effective alternatives that align better with their financial constraints.
Other issues with using Redshift includes is the vendor lockin which happens.
Other alternative options
In the below table we have highlighted the other options data lake options which could be used as an alternative to Redshift data lake. We have also mentioned the pros, cons and deployment methods.
| Advantage | Disadvantage | |
| Clickhouse (kubernetes or stand alone deployment possible) | Blazing Fast: Optimized for OLAP workloads. Cost-Effective: Open source with no licensing costs. Columnar Storage: Great for analytical queries. Easy Scaling: Supports distributed architectureNo vendor lock-in | Data Latency: May not handle real-time updates effectively. |
| Delta lake (Deploy on cloud or on-premise using Spark clusters (Databricks or self-managed Spark setup)). | Open Source: Built on Apache Spark with Delta Lake for ACID transactions. Scalability: Suitable for batch and real-time processing. Can handle PB of data also. ACID transactions supportCost-Effective: Leverages inexpensive storage like S3 or HDFS. Flexibility: Supports structured, semi-structured, and unstructured data.No vendor lock-inTime travel possible | Operational Overhead: Requires management of Spark clusters.Expertise Required: Steeper learning curve for non-Spark users.Performance: May require tuning for complex queries. |
| Google Big Query (Cloud managed service) | Serverless: No infrastructure management required.Pay-per-query model. High Performance: Optimized for analytical queries at scale. Integration: Excellent integration with Google Cloud services. | Data Ingress/Egress Costs: High costs if moving large amounts of data in and out.Vendor Lock-In: Tied to Google Cloud Platform.Complex Pricing: Can become expensive if queries are not optimized. |
| Apache Druid (can be deployed on prem or cloud, kubernetes support) | Real-Time Analytics: Designed for high-performance, low-latency queries. Open Source: Free with active community support. Scalable: Handles large-scale streaming and batch data. | Complex Setup: Requires expertise for optimal deployment and tuning. Specialized Use Case: Best suited for time-series and real-time analytics. Limited SQL: Not as SQL-friendly as traditional data warehouses. |
| S3 + Athena (fully managed service on cloud) | Cost-Effective: Pay only for storage in S3 and queries executed in Athena. Serverless: No infrastructure management required. Scalability: Can handle large datasets with ease. Integration: Works seamlessly with other AWS services. | Performance: Query performance can lag compared to purpose-built data warehouses like Redshift. Limited Query Optimization: For complex workloads, performance tuning is limited. Learning Curve: Requires understanding of data formats (Parquet, ORC) for cost optimization.Vendor lock in |
| Apache Hive (Can be deployed on-premise or on cloud platforms using Hadoop clusters) | Open Source: Free to use with a large community. Hadoop Ecosystem: Integrates well with Hadoop and HDFS. Compatibility: Supports SQL-like querying via HiveQL. Customizable: Highly configurable for diverse needs. | Performance: Slower compared to modern data warehouses. Complexity: Requires expertise in managing a Hadoop ecosystem. Resource-Intensive: High resource consumption for processing. |
Next Steps in building Data Lake
Once you have chosen a data lake platform, next steps is choosing the right data engineering tool which can help with ELT activities.
EL ( Extract and Load) : With data lake, as the first step we take the data from the various transactional platforms and load it into the data lake platform.
Transform (T) : As the second step, once we have all the necessary raw data in our data lake we need to process this data. This includes integration of the various collected data. Cleaning of the data like removing null values, removing outliers, fixing the data types etc. Further with transformation we would also do things like data wrangling, custom columns and calculations. Calculated tables can also be created.
Tool Selection
There are a lot of ETL/Data Engineering tools which are present in the market which can be used . These tools can automate and help with such EL & T activities. However the challenges with such Data engineering tools usage are
- Complex Setup and Configuration in case of on-premise deployment
- Steep learning curve to use these tools
- Dependency on using highly technical resources/ Data Engineers to do this kind of work
- Costly (both in terms of license cost and services implementation cost)
- Scalability Issues
- Limited Customization
- Maintenance Overhead
Ask On Data Usage: Ask On Data is world’s first chat based AI powered data engineering tool. With a very simple chat interface, powered by AI, it can help to create your data lake. It is present as a free open source version as well as paid version. In free open source version, you can download from Github and deploy on your own servers, whereas with enterprise version, you can use AskOnData as a managed service.
Advantages of using Ask On Data
- Built using advanced AI and LLM, hence there is no learning curve.
- Simply type and you can do the required transformations like cleaning, wrangling, transformations and loading
- No dependence on technical resources
- Super fast to implement (at the speed of typing)
- No technical knowledge required to use
Register here to learn more